The aim of the library is to ease the development, testing, and comparison of classification algorithms for multi-instance multi-label learning (MIML).
Use the package manager pip to install miml.
$ pip install mimllearning
The requirement packages for miml library are: numpy and scikit-learn. Installing miml with the package manager does not install the package dependencies. So install them with the package manager manually if not already downloaded.
$ pip install numpy
$ pip install scikit-learn
We can find the documentation of the project in this link: Documentation
from miml.data.load_datasets import load_dataset
dataset_train = load_dataset("miml_birds_random_80train.arff", from_library=True)
dataset_test = load_dataset("C:/Users/Damián/Desktop/miml_birds_random_20test.arff")
from miml.classifier import MIMLtoMIBRClassifier, AllPositiveAPRClassifier
classifier_mi = MIMLtoMIBRClassifier(AllPositiveAPRClassifier())
classifier_mi.fit(dataset_train)
results_mi=classifier_mi.evaluate(dataset_test)
probs_mi = classifier_mi.predict_proba(dataset_test)
from miml.report import Report
report = Report(results_mi, probs_mi, dataset_test)
report.to_string()
print("")
report.to_csv()
MIML library is released under the GNU General Public License GPLv3.
FAQs
MIML Learning Library
We found that mimllearning demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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